The Internet of Things (IoT) is a network of uniquely-identifiable, purposed “things” that are enabled to communicate data, pertaining to the things, between the things, over a communications network whereby the communicated data forms a basis for manipulating operation of the “things”. The “thing” in the “Internet of Things” could virtually be anything that fills a common purpose or use. For example, a “thing” could be a person with a heart rate monitor implant, a farm animal with a biochip transponder, an automobile that has built-in sensors to alert its driver when tire pressure is low, or the like, or any other natural or man-made entity that can be assigned a unique IP address and provided with the ability to transfer data over a communication network. Notably, if all the entities in an IoT are machines, then the IoT is referred to as a “Machine to Machine” (M2M) IoT or simply, as M2M IoT.
It is apparent from the aforementioned examples that an entity becomes a “thing” of an M2M IoT, when the entity has attached one or more sensors capable of (1) capturing one or more types of data pertaining thereto: (2) segregating the data (if applicable); (3) selectively communicating each segregation of data to one or more fellow “things”; (4) receiving one or more control commands (or instructions) from one or more fellow “things”, the one or more control commands is based on the data received by the one or more fellow “things”; and (5) executing one or more commands resulting in the manipulation or “management” of the operation of the corresponding entity. Therefore, in an IoT-enabled system, the “things” basically manage themselves without any human intervention, thus drastically improving the efficiency thereof.
U.S. Patent publication 2014/0336791 A1 discusses a predictive maintenance of industrial systems using big data analysis in a cloud platform.
U.S. Pat. No. 8,560,368 B1 discusses constraint-based scheduling, and in particular, constraint-based scheduling of one or more components for maintenance based on both, time-based maintenance information and condition-based maintenance information.
U.S. Pat. No. 6,405,108 B1 discusses a system and process for developing diagnostic algorithms for predicting impending failures of the subsystems in a locomotive.
WIPO application WO2005086760 A2 discusses a method and system for monitoring and maintaining equipment and machinery, as well as any other device or system that has discrete measuring points that can be gathered and analyzed to determine the status of the device or the system.
Visualization of analytical results or processed data from a big data system poses several new challenges in terms of scalability, volume and velocity. The results must be interpreted to the users, who are technicians and not familiar with many of the advanced sensor data analytics. Therefore visualization of the predictive maintenance results must be auto-interpreted to factory technicians using a simple, normalized gauge scale concept. None of the prior art technologies emphasize the visualization of the processed analytic data of predictive maintenance when obtained as a result of complex machine learning calculation.
However, existing prior art technologies are limited to rule based engines. Mere rule based engines do not provide effective visualization of the equipment monitoring data which is critical for operational deployment of predictive maintenance systems. Further, mere rule based engines may not be sufficient to help operators in handling multiple organ failure in machines. Further, the above prior art technologies does not allow scalability in order to handle large volumes of data and therefore not capable of providing the solution for an IoT based predictive maintenance system.
It is evident from the discussion of the aforementioned prior publications that none of the aforementioned prior art paves the way for predictive maintenance of a machine through an IoT system based classification and providing effective visualization to a machine operator. Therefore, there exists a need in the art for a solution to the aforementioned problem.